ML Engineer

CreatorIQ; Austin (TX); On-site; USD 132,000 - 165,000; Full time

Benefits offered by this job

15 days vacation

Wellness allowance

401(k) plan

Work-from-home stipend

Job summary

CreatorIQ is seeking a Machine Learning Engineer to join the Product Innovations team in Austin, Texas. This role involves deploying ML systems, building evaluation frameworks, and collaborating with Data Science on model quality decisions. The ideal candidate should have 4–7 years of experience in ML engineering and a strong understanding of Python and cloud deployment.

The position offers a competitive compensation range of $132K to $165K, flexible work schedules, and comprehensive health benefits.

Qualifications

• 4–7 years of professional software or ML engineering experience with 2+ years in production.
• Strong experience deploying and monitoring models in major cloud environments.
• Hands-on experience with NLP or ML systems.

Responsibilities

• Deploy and monitor ML systems in production, handling millions of records per day.
• Own the evaluation stack, including model evaluation frameworks.
• Partner with teams on annotation workflows and improve MLOps foundations.

Skills

Python

ML Engineering

NLP

Model Deployment

Cloud Platforms (AWS, GCP)

Job description

CreatorIQ is the operating system for creator‑led growth trusted by more than 1,300 global brands and agencies.

We’re on a mission to make businesses more human, and humans more impactful.

Machine Learning Engineer, Applied AI

As a MLE you’ll join our Product Innovations team and work across the full applied ML stack — deploying models, building evaluation systems, and making data and infrastructure decisions that turn experimental data science into cost‑efficient products. You’ll partner closely with our Data Science and Engineering teams on our vector embeddings ecosystem, ground‑truth pipelines, model evaluation, and pre/post‑processing decisions that determine product quality.

What You’ll Do

• Deploy and monitor ML systems in production, from classic NLP and embedding models to LLM‑powered features – handling millions of records per day.
• Own the evaluation stack: golden datasets, model‑as‑a‑judge frameworks, inter‑annotator agreement, and regression tests that gate releases.
• Build and maintain our vector embeddings ecosystem and the retrieval, classification, and similarity patterns that sit on top of it.
• Partner with Data Science on annotation workflows, PII scrubbing, and ground‑truth pipelines.
• Improve our MLOps foundations—versioning, observability, drift detection—to enable faster shipping.
• Translate fuzzy product problems into measurable AI features with clear success criteria.

What You’ve Done

• 4–7 years of professional software or ML engineering experience, including 2+ years shipping ML systems to production.
• Strong Python; comfort with the modern data/ML stack.
• Hands‑on experience deploying and monitoring models in at least one major cloud (AWS or GCP); willingness to learn the other.
• Production experience with NLP or ML systems—classification, NER, embeddings, ranking, similarity, or LLM‑powered features (a mix of traditional ML and LLM work is acceptable).
• Practical experience with evaluation for ML or LLM systems—golden datasets, model‑as‑a‑judge, IAA, precision/recall, or equivalent.
• Collaborative communicator who works well with data scientists and engineers, and can clearly explain ideas, requirements, and trade‑offs to non‑technical stakeholders.

Bonus

• Experience with vector databases or retrieval systems at scale.
• Experience with managed ML services on AWS (SageMaker) and/or GCP (Vertex AI).
• Annotation workflow experience (Label Studio, Scale AI, or similar) and a point of view on inter‑annotator agreement.
• Familiarity with PII scrubbing patterns and privacy‑by‑design data handling.
• Open‑source contributions, blog posts, or talks on LLM/embedding production work.

What You Will Get From Us

• People: work with talented, collaborative, and friendly people who love what they do.
• Guidance: utilize our learning platform to get the training and tools you’ll need to succeed from day one.
• Surprise meal stipends.
• Work/life harmony: 15 days vacation, floating and set holidays, wellness allowance, and paid parental leave.
• Whole Health Package: medical, dental, vision, life, disability insurance, and more.
• Savings: a 401(k) plan to help you plan ahead.
• Work‑from‑home stipend to set up a home office (or buy a new dog leash – your choice).

Compensation

Compensation Range: $132K – $165K.

AI Transparency Notice

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications and note‑taking during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please refer to our Global Candidate Privacy Notice.

Commitment to Diversity and Inclusion

At CreatorIQ, we believe that diversity is the key to unlocking our full potential. We are committed to fostering an inclusive, equitable, and empowering work environment where everyone can thrive, regardless of race, ethnicity, gender, sexual orientation, age, religion, disability, or any other characteristic that makes us unique.